The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
As the network technologies develop rapidly, the number of users and new services keeps on expanding, and network operators have to try to provide the best services for users to survive in the market full of intense competition. Therefore, network performance has become the focus in such a circumstance. In practical applications, an operator usually needs to run network environment simulation to evaluate the network performance for network planning, optimization and Quality of Service (QoS) control. According to the operation principles of actual networks, network performance model can be established in a network environment simulation, and an actual network environment can be simulated by using the established network performance model.
The network performance model in the existing technology is the Gaussian mixture model. The basic process of establishing a Gaussian mixture model includes firstly a step of providing performance data by a part of network nodes in a network. The performance data is described with plurality of components which affect the performance data. Each of the components is in compliance with Gaussian distribution. Therefore the components are generally called Gaussian components. The performance data in the established Gaussian mixture model equals the total weights of all Gaussian components. Suppose a piece of performance data is described with N Gaussian components, the Gaussian distribution mean value of the No. j Gaussian component is μj, the deviation of the Gaussian component is σj2 and the mixture weight value of the Gaussian component is ωj, the probability density function of the performance data is
      p    ⁡          (              s        ❘        θ            )        =            ∑              j        =        1            N        ⁢                  ⁢                  ω        j            ⁢                                    N            s                    ⁡                      (                                          μ                j                            ,                              σ                j                2                                      )                          .            Wherein θ=(ωj,μj,σj2) and s is the performance data. The probability density function shows the probability density of the performance data when the Gaussian components of the performance data are determined. A matrix can be obtained with performance data on rows and Gaussian components on columns. A value in the matrix is the probability density corresponding to the performance data of the corresponding row and the Gaussian component of the corresponding column. Therefore the matrix shows the distribution of the Gaussian components of all measured performance data. A simulation environment can be established with the matrix as the parameters of the network performance model.
However, when the Gaussian mixture model is used as the network performance model, the Gaussian component weights are derived solely from sample performance data provided by the network nodes, i.e., the network performance model is established for the network nodes that provided the performance data and is reliable only in showing the performance of the network nodes. The performance data of other network nodes in the network are not shown in the network performance model, which means the network performance model established with the conventional method is not suitable to the whole network and is not reliable in showing the performance of the whole network. In one sentence, the network performance model does not fit the whole network consisting of network nodes of same aggregation features, and nodes providing the performance data can be chosen at random from the aggregation space.